T-SQL Tuesday #97: Learning Goals for 2018

Well, considering another calendar year is coming, and there’s a T-SQL Tuesday post theme about learning goals (something I haven’t tackled in a while), it’s about time I write another blog post. Time to shift the motivator to HYPERTHRUST.

I’m back to blog on T-SQL Tuesday for a topic prompted by Malathi Mahadevan (b|t). This month’s theme is simple on the surface – setting our learning goals for 2018. However, the hard part is and will always be execution. I often get overcome by struggles with time management, and then I don’t show what I learned to the public out of a fear of impostor syndrome. Over the last two years, I went back and learned some stuff about indexes and execution plans that I overlooked in the past. I also set out to get myself acquainted more with R, while refreshing my Python skills (especially Pandas) and fumbling around my own virtual Linux box until I crashed it. I think the results have been adequate, but application of these skills left me questioning if these accomplishments were useful. Plus, there eventually was a dominant focus on my running ambitions.

So first, I will need to consider limitations and how to handle my time.

I’m not always consistent with carving time for grad school homework, but now I will need to be so in order to complete classes. I want a base of one hour per day to do these lessons. Lab time will also count, as I learn best when I can apply examples and do some trial and error.

I have already signed up for ITIL certification, so that’s already on the docket and is a requirement for my gig.

Redesigning my “home lab” will be an effort where I will need to ask for advice. More on that below.

What do you want to learn?

My current position focuses on data architecture and modeling, working on data warehouse development and master data management, and ETL processes where I’m mostly comfortable. That’s a logical starting point, right? With all that in mind, here are the goals I have outlined thus far.

The MBA program and ITIL are already among my goals, and there’s a clear path already for each.

Going back to my point in the intro, it’s not always about learning a technology. One may already know some of the skills needed, but they forget the base knowledge that’s required to build around. As someone with a developer/analyst background, I want to focus on more DBA fundamentals. I have often been isolated from that end, in part because I prefer to not become one myself, but I would like to figure out the administrative capability that is key to building the best code and EDW possible.

I haven’t quite touched big data, but considering the direction we’re headed as a group and an industry, the suggestions others gave me should be heeded and I should learn about the Apache family of products. Besides, one of my MBA classes for the spring/summer is a Big Data class. Picked that one for a reason.

In terms of data science, I’ll be aiming to call myself a mid-level Python and mid-level R programmer, especially since both are now integrated into SQL Server. As someone who has mostly worked at companies who use the Microsoft stack (which is why this post is here), I’m happy that they have started to embrace open source technology. I think the Python/R will be good for both data cleansing (essential to the EDW) and machine learning.

My home lab is currently run off my desktop, and sometimes ported to a real mediocre laptop since I avoid using my work server for anything. So this is where I ask a question of the online community: should I just get the virtual box? Should I apply containers? I began dabbling with GitHub and want to figure out how to keep my scripts stored. In summary, I want to learn the best practices for a data lab environment.

A reach goal: actually set up a warehouse on Azure

I finally want to take some time to get reacquainted with MySQL. At my work, we do have MySQL on a few servers, which we use in tandem with the enterprise SQL Server 2012-2017 environments. I’m particularly interested in the migration aspect between the two.

Over the last three months I have been motivated again on a professional level, so I’m ready to structure learning to make next year the most awesome.

How and when do you want to learn?

I’ll start by saying that I have access to a few classes thanks to a very special connection. Online classrooms will apply for the R, Python, and Apache goals in particular, where practical examples and homework will be given as part of the units. My focus is already on three classes on Udemy, while the certification can be done via EdX. What are everyone’s thoughts on Pluralsight if you’re a Business Intelligence Architecture person? I do still need to conisder this as an option.

The networking aspect is always tricky to get started and bluntly ask a question. There’s a SQL community Slack which I’ve used very, very sparingly, but will need to turn to more to ask these best-practices questions for the lab and the DBA fundamentals. If you see me ask this in the general forum, you’ll know where it came from.

Speaking of DBA fundamentals – there’s a group for it. I’m signed up for the emails, but need to actually watch the recordings more when I can truly focus on lessons from them. I’ll also be referencing three versions of the Stairways series. Let’s not forget all the area user groups and some SQL Saturdays that line up with my calendar and location, and all the blogs that can be accessed anywhere. Those things will continue.

How do you plan to improve on what you learned?

Everything related to the data warehouse or even MDM application can be used in my job. Some of the other aspects that I do out of curiosity can also be applied tangentially. The obvious first response is that I can use this at work.

I learn a ton from the speakers I have seen over the years, and about one year ago I became the speaker for the first time at our local TriPASS user group. Speaking is a good way to test what you’ve learned while giving back to others. Well, I don’t plan to go touring the country like some of them, and I also want to work on how to apply skills in my home area of the Research Triangle, which has great civic groups that I need to involve myself in more often including one of the hacking conferences in full, not part. So I will be working on two new talks to use locally first, where experts can provide critiques and show me where I can improve, and then maybe I can throw my hat in for virtual talks. I haven’t set aside what I want to talk about, as that goal should be based on incremental progress and comfort as I build up my local and national network.

Blogging, and the question of frequency, is addressed in the host post. Looking at my 2017 goals I spectacularly failed on blogging. I’ll simply note that I hope to blog more on those technical elements in the next year as I learn them, and blocking aside a set time duration to improve my focus and dedication to the writing. How will I show this off outside of a class? I have been collecting some race data for the Triangle that I have been adding to a database. This has already allowed me to sharpen my ETL skills further while setting up for some good analysis work. My goal is to have this database ready for analysis by end of March.

Setting goals is one thing, but discipline matters more. I think back on how I grew as a runner over time by gathering the basics that I didn’t know existed. To wear the right clothes and to work on mechanics. To keep easy runs easy and allow for enough recovery between quality runs. I have started to realize that it’s a good analogy for growth in the industry that I also love. The speaking engagements and technical blog posts are like races where you show what prep did. Simultaneously training in multiple subjects can be similar to doing speed work and 5k races while prepping for a full marathon or even a triathlon. The class units and sessions we attend can be easy runs, with hands-on labs similar to track work. Maybe taking that certification test represents the marathon. To some, maybe getting into PASS Summit is their Boston qualifier.

However, getting better at anything like running or data platforms requires emphasizing the basics before you can apply the advanced lessons to the best of your ability. Regardless on if you are classically taught on a programming language or a tool, or if you had to figure it out all for yourself, you never know if you may be missing some best practice foundations. More than anything, my learning goal for 2018 is to bridge that gap where possible and necessary, while welcoming advice on where to focus or refocus my learning strategy so that I can become better as a data ninja. You know what Schoolhouse Rocky says…